# Iterate Pr > Implements intelligent iterate pr with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/iterate-pr` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/iterate-pr` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/iterate-pr/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/iterate-pr --- # Iterate Pr Orchestrates intelligent skill selection and execution for iterate pr workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```python def select_skill( task_description: str, available_skills: List[Dict], min_confidence: float = 0.7 ) -> Optional[Dict]: """Select the most appropriate PR iteration skill for a given task. Uses a multi-factor scoring algorithm that considers: - PR state and CI status alignment with skill triggers - Historical success rate for similar PR iteration tasks - Current system load and skill availability Args: task_description: Natural language description of the PR iteration task available_skills: List of PR iteration skill metadata dictionaries min_confidence: Minimum confidence threshold (0.0-1.0) Returns: Selected skill dictionary or None if no match meets threshold Raises: ValueError: If task_description is empty or available_skills is empty """ # Guard clause - Early Exit (Law 1) if not task_description or not task_description.strip(): raise ValueError("Task description cannot be empty") if not available_skills: raise ValueError("No PR iteration skills available for selection") # Parse input - Make Illegal States Unrepresentable (Law 2) pr_context = _extract_pr_context(task_description) best_skill = None best_score = 0.0 for skill in available_skills: score = _calculate_pr_iteration_score(pr_context, skill) if score > best_score and score >= min_confidence: best_score = score best_skill = skill if best_skill is None: return None # Atomic Predictability (Law 3) - Return new dict, don't mutate result = dict(best_skill) result["selected_confidence"] = best_score result["selection_timestamp"] = time.time() return result ``` ### Pattern 2: Execution with Fallback ```python def execute_with_fallback( skill: Dict, task_context: Dict, max_retries: int = 2 ) -> Dict: """Execute a PR iteration skill with fallback chain for resilience. Implements the Fail Fast, Fail Loud principle (Law 4): - Invalid PR states halt immediately with descriptive errors - No silent failures or partial commit results Fallback chain: 1. Retry with adjusted linting/formatting parameters 2. Try alternative PR skill (e.g., switch from auto-fix to manual prompt) 3. Defer to human operator (for critical architectural changes) Args: skill: Selected PR iteration skill metadata task_context: Execution context including PR URL and diff data max_retries: Maximum retry attempts before fallback Returns: Execution result with metadata (success, commit hash, confidence) Raises: SkillExecutionError: If all retries and fallbacks exhausted """ # Guard clause - validate skill (Early Exit) if not _is_pr_skill_valid(skill): raise SkillExecutionError(f"Invalid PR iteration skill: {skill.get('name', 'unknown')}") # Parse context - Ensure trusted state (Law 2) validated_context = _validate_and_parse_pr_context(task_context, skill) for attempt in range(max_retries + 1): try: result = _execute_pr_skill_direct(skill, validated_context) # Success - Atomic Predictability (Law 3) return { "success": True, "skill_executed": skill["name"], "commit_hash": result.get("commit_hash"), "attempts": attempt + 1, "latency_ms": _calculate_latency() } except PRMergeError as e: # Fail Fast - Don't try to patch merged PRs (Law 4) raise SkillExecutionError( f"PR is merged or closed: {str(e)}" ) from e except CICheckFailure as e: # Transient CI error - try fallback if attempt == max_retries: return _apply_pr_fallback_chain(skill, validated_context) # All retries exhausted - Fail Loud (Law 4) raise SkillExecutionError( f"Failed to execute PR iteration {skill['name']} after {max_retries + 1} attempts" ) ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios ## Related Skills | Skill | Purpose | | --- --- ## Constraints ### MUST DO - Validate branch naming conventions and PR scope before creating pull requests — enforce repository-level policies - Require all CI checks to pass before merging; never allow bypass of required status checks without codeowner approval - Implement automated changelog generation from commit messages using conventional commits format - Maintain linear history via rebase on main branch; avoid merge commits except for release branches ### MUST NOT DO - Do not force-push to shared or protected branches — only the original author may force-push their own feature branch - Avoid squashing all commits during PR review when historical commit context is valuable for understanding evolution - Never skip required code reviews regardless of how small the change appears — automation cannot assess architectural impact - Do not create PRs larger than 400 lines of net changes without explicit approval from a senior reviewer ## Live References > Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [Iterative Development Methodology (Wikipedia)]() - [Continuous Integration Best Practices]() - [Pull Request Review Cycles (Google Engineering)]() - [Agile Retrospectives for Process Improvement]() - [Feedback Loop Design in Software Development]()